Agreed. There is deep potential for ML in healthcare. We need more contributors advancing research in this space. One opportunity as people look around: many priors merit reconsideration.

For instance, genomic data that may seem identical may not actually be identical. In classic biological representations (FASTA), canonical cytosine and methylated cytosine are both collapsed into the letter "C" even though differences may spur differential gene expression.

What's the optimal tokenization algorithm and architecture for genomic models? How about protein binding prediction? Unclear!

There are so many open questions in biomedical ML.

The openness-impact ratio is arguably as high in biomedicine as anywhere else: if you help answer some of these questions, you could save lives.

Hopefully, awesome frameworks like this lower barriers and attract more people.

I see it a bit differently - LLMs are an incredible innovation but it’s hard to do anything useful with them without the right wrapper.

A good wrapper has deep domain knowledge baked into it, combined with automation and expert use of the LLM.

It maybe isn’t super innovative but it’s a bit of an art form and unlocks the utility of the underlying LLM

> We’re seeing endless waves of specialized wrappers around LLM API calls.

AFAIK, doing proper RAG is much, much more than this.

What's your technical background if you don't mind me asking?

The application of a new technology to new fields always looks like this. SQL databases become widespread, there's a wave of specialized software development for business practices. The internet becomes widespread, and there's a wave of SaaS solving specialized use cases.

We are going to see the same for anything that Claude or similar can't handle out of the box.

By that argument every SaaS is a db wrapper

Think of it this way: before the internal combustion engine people used animal power, steam power, human power, wind power, etc to move cargo, passengers, and even specialized loads like water pumps for the fire brigade. Then with internal combustion they did those things faster and at greater scale. That wasn't innovating on the ICE itself, or solving new problems. But it was still useful. Of course they also eventually did innovate on the ICE, and they solved new problems with it(heavier than air flight, for example) but it took awhile.

I suspect it's jumping on the hype train. Especially since its from a big Uni. Funding in research is all about marketing and latching onto the right keywords (just like VC really) so the most successful researchers are those who can market themselves effectively. Whether this tool is actually any good is secondary to whether it achieves the real goal of getting future funding for it's author.

Creating a biological weapon requires a whole bunch of unique and specialised skills, equipment, safety measures (so you don't infect/kill yourself/your people) and even multidisciplinary skill sets. Take for example the Kameido (Japan) incident by the Aum Shinrikyo cult/religious group [1]. Same group which committed the Sarin attack [2].

> The use of an attenuated B. anthracis strain, low spore concentrations, ineffective dispersal, a clogged spray device, and inactivation of the spores by sunlight are all likely contributing factors to the lack of human cases.

Now you may say, that's bacteria, what about viruses? A similar set of problems would arise, how do you successfully grow virus to high titers? Even vaccine companies struggle to do this with certain viruses. Then the issue of dispersal, infectivity and mortality arise (too quick, it kills the host without spreading and authorities will notice, too slow, same problem: authorities will notice). I haven't even mentioned biological engineering which requires years of technical knowledge and laboratory experience combined with a intimate knowledge of the organism you're working with.

What worries me the most is nature springing a new influenza subtype. Our farming practices, especially in developing countries, is bound to breed a new subtype. It happened in 2009 (H1N1pdm) and it is bound to happen again. We got lucky with H1N1pdm.

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC3322761/ 2. https://en.wikipedia.org/wiki/Tokyo_subway_sarin_attack

There's an inner ToolRetriever which is a LLM call to select the most relevant tools/data/libraries.